The angular resolution of radar is of crucial signifi-cance to its tracking performance.In this paper,a super-resolu-tion parameter estimation algorithm based on wide-narrowband joint processing is proposed to improve...The angular resolution of radar is of crucial signifi-cance to its tracking performance.In this paper,a super-resolu-tion parameter estimation algorithm based on wide-narrowband joint processing is proposed to improve the angular resolution of wideband monopulse radar.The range cells containing resolv-able scattering points are detected in the wideband mode,and these range cells are adopted to estimate part of the target parameters by algorithms of low computational requirement.Then,the likelihood function of the echo is constructed in the narrow-band mode to estimate the rest of the parameters,and the parameters estimated in the wideband mode are employed to reduce computation and enhance estimation accuracy.Simu-lation results demonstrate that the proposed algorithm has higher estimation accuracy and lower computational complexity than the current algorithm and can avoid the risk of model mis-match.展开更多
A full-polarimetric super-resolution algorithm with spatial smoothing processing is presented for one-dimensional（1-D）radar imaging.The coherence between scattering centers is minimized by using spatial smoothing pr...A full-polarimetric super-resolution algorithm with spatial smoothing processing is presented for one-dimensional（1-D）radar imaging.The coherence between scattering centers is minimized by using spatial smoothing processing（SSP）.Then the range and polarimetric scattering matrix of the scattering centers are estimated.The impact of different lengths of the smoothing window on the imaging quality is mainly analyzed with different signal-to-noise ratios（SNR）.Simulation and experimental results show that an improved radar super-resolution range profile and more precise estimation can be obtained by adjusting the length of the smoothing window under different SNR conditions.展开更多
The application of image super-resolution(SR)has brought significant assistance in the medical field,aiding doctors to make more precise diagnoses.However,solely relying on a convolutional neural network(CNN)for image...The application of image super-resolution(SR)has brought significant assistance in the medical field,aiding doctors to make more precise diagnoses.However,solely relying on a convolutional neural network(CNN)for image SR may lead to issues such as blurry details and excessive smoothness.To address the limitations,we proposed an algorithm based on the generative adversarial network(GAN)framework.In the generator network,three different sizes of convolutions connected by a residual dense structure were used to extract detailed features,and an attention mechanism combined with dual channel and spatial information was applied to concentrate the computing power on crucial areas.In the discriminator network,using InstanceNorm to normalize tensors sped up the training process while retaining feature information.The experimental results demonstrate that our algorithm achieves higher peak signal-to-noise ratio(PSNR)and structural similarity index measure(SSIM)compared to other methods,resulting in an improved visual quality.展开更多
A self-made single-roll stirring (SRS) machine was used to manufacturesemisolid A2017 alloy, the mechanism of A2017 alloy formation was investigated. It was shown thatA2017 dendrites growing on the rough roll surface ...A self-made single-roll stirring (SRS) machine was used to manufacturesemisolid A2017 alloy, the mechanism of A2017 alloy formation was investigated. It was shown thatA2017 dendrites growing on the rough roll surface are crashed into fragments by the roll, which moveand grow freely then contribute the formation of finer spherical microstruc-ture. When casting at710-750℃, fine and homogeneous spherical or elliptical grains of A2017 alloy were obtained.Extending forming mould has been designed and was installed at the exit of roll-shoe gap. A2017alloy was formed by extending continuously at the semisolid state on SRS machine. Throughcontrolling pouring temperature, semisolid forming and extending extrusion was combined organically.A2017 product with fine surface and rectangular transection of 14 mm x 25 mm was obtained. Bycontrast to the national standard, the fracture strength and elongation of A2017 products producedfrom extending semisolid extrusion have been improved with an increase of 100 MPa and 29%,respectively.展开更多
Previous deep learning-based super-resolution(SR)methods rely on the assumption that the degradation process is predefined(e.g.,bicubic downsampling).Thus,their performance would suffer from deterioration if the real ...Previous deep learning-based super-resolution(SR)methods rely on the assumption that the degradation process is predefined(e.g.,bicubic downsampling).Thus,their performance would suffer from deterioration if the real degradation is not consistent with the assumption.To deal with real-world scenarios,existing blind SR methods are committed to estimating both the degradation and the super-resolved image with an extra loss or iterative scheme.However,degradation estimation that requires more computation would result in limited SR performance due to the accumulated estimation errors.In this paper,we propose a contrastive regularization built upon contrastive learning to exploit both the information of blurry images and clear images as negative and positive samples,respectively.Contrastive regularization ensures that the restored image is pulled closer to the clear image and pushed far away from the blurry image in the representation space.Furthermore,instead of estimating the degradation,we extract global statistical prior information to capture the character of the distortion.Considering the coupling between the degradation and the low-resolution image,we embed the global prior into the distortion-specific SR network to make our method adaptive to the changes of distortions.We term our distortion-specific network with contrastive regularization as CRDNet.The extensive experiments on synthetic and realworld scenes demonstrate that our lightweight CRDNet surpasses state-of-the-art blind super-resolution approaches.展开更多
The effects of Sr addition and pressure increase on the microstructure and casting defects of a low-pressure die cast （LPDC） AISi7Mg0.3 alloy have been studied. Metallographic and image analysis techniques have been...The effects of Sr addition and pressure increase on the microstructure and casting defects of a low-pressure die cast （LPDC） AISi7Mg0.3 alloy have been studied. Metallographic and image analysis techniques have been used to quantitatively examine the microstructural changes and the amount of porosity occurring at different Sr levels and pressure parameters. The results indicate that an increase in the filling pressure induces lower heat dissipation of the liquid close to the die/core surfaces, with the formation of slightly greater dendrite arms and coarser eutectic Si particles. On the other hand, the increase in the Sr level leads to finer microstructural scale and eutectic Si. The analysed variables, within the experimental conditions, do not affect the morphology of eutectic Si particles. Higher applied pressure and Sr content generate castings with lower amount of porosiW. However, as the filling pressure increases the flow of metal inside the die cavity is more turbulent, leading to the formation of oxide films and cold shots. In the analysed range of experimental conditions, the design of experiment methodology and the analysis of variance have been used to develop statistical models that accurately predict the average size of secondary dendrite arm spacing and the amount of porosity in the low-pressure die cast AISiTMg0.3 alloy.展开更多
Sparse representation has attracted extensive attention and performed well on image super-resolution（SR） in the last decade. However, many current image SR methods face the contradiction of detail recovery and artif...Sparse representation has attracted extensive attention and performed well on image super-resolution（SR） in the last decade. However, many current image SR methods face the contradiction of detail recovery and artifact suppression. We propose a multi-resolution dictionary learning（MRDL） model to solve this contradiction, and give a fast single image SR method based on the MRDL model. To obtain the MRDL model, we first extract multi-scale patches by using our proposed adaptive patch partition method（APPM）. The APPM divides images into patches of different sizes according to their detail richness. Then, the multiresolution dictionary pairs, which contain structural primitives of various resolutions, can be trained from these multi-scale patches.Owing to the MRDL strategy, our SR algorithm not only recovers details well, with less jag and noise, but also significantly improves the computational efficiency. Experimental results validate that our algorithm performs better than other SR methods in evaluation metrics and visual perception.展开更多
Image super-resolution(SR)is an important technique for improving the resolution and quality of images.With the great progress of deep learning,image super-resolution achieves remarkable improvements recently.In this ...Image super-resolution(SR)is an important technique for improving the resolution and quality of images.With the great progress of deep learning,image super-resolution achieves remarkable improvements recently.In this work,a brief survey on recent advances of deep learning based single image super-resolution methods is systematically described.The existing studies of SR techniques are roughly grouped into ten major categories.Besides,some other important issues are also introduced,such as publicly available benchmark datasets and performance evaluation metrics.Finally,this survey is concluded by highlighting four future trends.展开更多
Super-Resolution (SR) technique means to reconstruct High-Resolution (HR) images from a sequence of Low-Resolution (LR) observations,which has been a great focus for compressed video. Based on the theory of Projection...Super-Resolution (SR) technique means to reconstruct High-Resolution (HR) images from a sequence of Low-Resolution (LR) observations,which has been a great focus for compressed video. Based on the theory of Projection Onto Convex Set (POCS),this paper constructs Quantization Constraint Set (QCS) using the quantization information extracted from the video bit stream. By combining the statistical properties of image and the Human Visual System (HVS),a novel Adaptive Quantization Constraint Set (AQCS) is proposed. Simulation results show that AQCS-based SR al-gorithm converges at a fast rate and obtains better performance in both objective and subjective quality,which is applicable for compressed video.展开更多
A maximum a posteriori（ MAP） algorithm is proposed to improve the accuracy of super resolution（ SR） reconstruction in traditional methods. The algorithm applies both joints image registration and SR reconstruction...A maximum a posteriori（ MAP） algorithm is proposed to improve the accuracy of super resolution（ SR） reconstruction in traditional methods. The algorithm applies both joints image registration and SR reconstruction in the framework,but separates them in the process of iteratiion. Firstly,we estimate the shifting parameters through two lowresolution（ LR） images and use the parameters to reconstruct initial HR images. Then,we update the shifting parameters using HR images. The aforementioned steps are repeated until the ideal HR images are obtained. The metrics such as PSNR and SSIM are used to fully evaluate the quality of the reconstructed image. Experimental results indicate that the proposed method can enhance image resolution efficiently.展开更多
In this work, we describe a new multiframe Super-Resolution（SR） framework based on time-scale adaptive Normalized Convolution（NC）, and apply it to astronomical images. The method mainly uses the conceptual basis o...In this work, we describe a new multiframe Super-Resolution（SR） framework based on time-scale adaptive Normalized Convolution（NC）, and apply it to astronomical images. The method mainly uses the conceptual basis of NC where each neighborhood of a signal is expressed in terms of the corresponding subspace expanded by the chosen polynomial basis function. Instead of the conventional NC, the introduced spatially adaptive filtering kernel is utilized as the applicability function of shape-adaptive NC, which fits the local image structure information including shape and orientation. This makes it possible to obtain image patches with the same modality,which are collected for polynomial expansion to maximize the signal-to-noise ratio and suppress aliasing artifacts across lines and edges. The robust signal certainty takes the confidence value at each point into account before a local polynomial expansion to minimize the influence of outliers.Finally, the temporal scale applicability is considered to omit accurate motion estimation since it is easy to result in annoying registration errors in real astronomical applications. Excellent SR reconstruction capability of the time-scale adaptive NC is demonstrated through fundamental experiments on both synthetic images and real astronomical images when compared with other SR reconstruction methods.展开更多
The signal direction of arrival (DOA) estimate algorithm based on the eigendecomposition of the modified covariance matrix is introduced in this paper. A field test system consisting of a 4-element linear array and a ...The signal direction of arrival (DOA) estimate algorithm based on the eigendecomposition of the modified covariance matrix is introduced in this paper. A field test system consisting of a 4-element linear array and a meter band radar is also presented, which is applied to the experimental studies of the algorithms in the practical performances. The results of the test indicate that when SNR is only 5.85 dB, two airplanes being 0.25 beam width apart in azimuth can be resolved clearly.展开更多
Time-synchronous-averaging(TSA)is based on the idea of denoising by averaging,and it extracts the periodic components of a quasiperiodic signal and keeps the extracted waveform undistorted.This paper studies the mathe...Time-synchronous-averaging(TSA)is based on the idea of denoising by averaging,and it extracts the periodic components of a quasiperiodic signal and keeps the extracted waveform undistorted.This paper studies the mathematical properties of TSA,where three propositions are given to reveal the nature of TSA.This paper also proposes a TSA-spectrum based on super-resolution analysis and it decomposes a signal without using any base function.In contrast to discrete Fourier transform spectrum(DFT-spectrum),which is a spectrum in frequency domain,TSA-spectrum is a period-based spectrum,which can present more details of the cross effects between different periodic components of a quasiperiodic signal.Finally,a case study is carried out using bearing fault analysis to illustrate the performance of TSA-spectrum,where the rotation speed fluctuation of the shaft is estimated,which is about 0.12 ms difference.The extracted fault signals are presented and some insights are provided.We believe that this paper can provide new motivation for TSA-spectrum to be widely used in applications involving quasiperiodic signal processing(QSP).展开更多
Although most of the existing image super-resolution(SR)methods have achieved superior performance,contrastive learning for high-level tasks has not been fully utilized in the existing image SR methods based on deep l...Although most of the existing image super-resolution(SR)methods have achieved superior performance,contrastive learning for high-level tasks has not been fully utilized in the existing image SR methods based on deep learning.This work focuses on two well-known strategies developed for lightweight and robust SR,i.e.,contrastive learning and feedback mechanism,and proposes an integrated solution called a split-based feedback network(SPFBN).The proposed SPFBN is based on a feedback mechanism to learn abstract representations and uses contrastive learning to explore high information in the representation space.Specifically,this work first uses hidden states and constraints in recurrent neural network(RNN)to implement a feedback mechanism.Then,use contrastive learning to perform representation learning to obtain high-level information by pushing the final image to the intermediate images and pulling the final SR image to the high-resolution image.Besides,a split-based feedback block(SPFB)is proposed to reduce model redundancy,which tolerates features with similar patterns but requires fewer parameters.Extensive experimental results demonstrate the superiority of the proposed method in comparison with the state-of-the-art methods.Moreover,this work extends the experiment to prove the effectiveness of this method and shows better overall reconstruction quality.展开更多
Convolutional neural networks(CNNs) have shown great potential for image super-resolution(SR).However,most existing CNNs only reconstruct images in the spatial domain,resulting in insufficient high-frequency details o...Convolutional neural networks(CNNs) have shown great potential for image super-resolution(SR).However,most existing CNNs only reconstruct images in the spatial domain,resulting in insufficient high-frequency details of reconstructed images.To address this issue,a channel attention based wavelet cascaded network for image super-resolution(CWSR) is proposed.Specifically,a second-order channel attention(SOCA) mechanism is incorporated into the network,and the covariance matrix normalization is utilized to explore interdependencies between channel-wise features.Then,to boost the quality of residual features,the non-local module is adopted to further improve the global information integration ability of the network.Finally,taking the image loss in the spatial and wavelet domains into account,a dual-constrained loss function is proposed to optimize the network.Experimental results illustrate that CWSR outperforms several state-of-the-art methods in terms of both visual quality and quantitative metrics.展开更多
Ca0.4Sr0.6Bi3.95Nd0.05Ti4O15 （C0.4S0.6BNT） ferroelectric thin films were prepared on Pt/Ti/SiO2/Si substrates by sol-gel method. Effect of annealing process （time and temperature） on structures and ferroelectric p...Ca0.4Sr0.6Bi3.95Nd0.05Ti4O15 （C0.4S0.6BNT） ferroelectric thin films were prepared on Pt/Ti/SiO2/Si substrates by sol-gel method. Effect of annealing process （time and temperature） on structures and ferroelectric properties of C0.4S0.6BNT thin film was investigated. The relative intensity of （200） peak increased first then decreased with annealing temperature and became predominant at 800 ℃. In contrast, no evident change could be observed in the （001） peak. The remnant polarization （Pr） and coercive field （Ec） for C0.4S0.6BNT film annealed at 800℃ for 5 min were 21.6μC/cm2 and 68.3 kV/cm, respectively.展开更多
文摘The angular resolution of radar is of crucial signifi-cance to its tracking performance.In this paper,a super-resolu-tion parameter estimation algorithm based on wide-narrowband joint processing is proposed to improve the angular resolution of wideband monopulse radar.The range cells containing resolv-able scattering points are detected in the wideband mode,and these range cells are adopted to estimate part of the target parameters by algorithms of low computational requirement.Then,the likelihood function of the echo is constructed in the narrow-band mode to estimate the rest of the parameters,and the parameters estimated in the wideband mode are employed to reduce computation and enhance estimation accuracy.Simu-lation results demonstrate that the proposed algorithm has higher estimation accuracy and lower computational complexity than the current algorithm and can avoid the risk of model mis-match.
基金Supported by the National Naturral Science Foundation of China(61301191)
文摘A full-polarimetric super-resolution algorithm with spatial smoothing processing is presented for one-dimensional（1-D）radar imaging.The coherence between scattering centers is minimized by using spatial smoothing processing（SSP）.Then the range and polarimetric scattering matrix of the scattering centers are estimated.The impact of different lengths of the smoothing window on the imaging quality is mainly analyzed with different signal-to-noise ratios（SNR）.Simulation and experimental results show that an improved radar super-resolution range profile and more precise estimation can be obtained by adjusting the length of the smoothing window under different SNR conditions.
文摘The application of image super-resolution(SR)has brought significant assistance in the medical field,aiding doctors to make more precise diagnoses.However,solely relying on a convolutional neural network(CNN)for image SR may lead to issues such as blurry details and excessive smoothness.To address the limitations,we proposed an algorithm based on the generative adversarial network(GAN)framework.In the generator network,three different sizes of convolutions connected by a residual dense structure were used to extract detailed features,and an attention mechanism combined with dual channel and spatial information was applied to concentrate the computing power on crucial areas.In the discriminator network,using InstanceNorm to normalize tensors sped up the training process while retaining feature information.The experimental results demonstrate that our algorithm achieves higher peak signal-to-noise ratio(PSNR)and structural similarity index measure(SSIM)compared to other methods,resulting in an improved visual quality.
基金This project is financially supported by State Key Fundamental Research of "973" Development Plan (No. G2000067208-4)
文摘A self-made single-roll stirring (SRS) machine was used to manufacturesemisolid A2017 alloy, the mechanism of A2017 alloy formation was investigated. It was shown thatA2017 dendrites growing on the rough roll surface are crashed into fragments by the roll, which moveand grow freely then contribute the formation of finer spherical microstruc-ture. When casting at710-750℃, fine and homogeneous spherical or elliptical grains of A2017 alloy were obtained.Extending forming mould has been designed and was installed at the exit of roll-shoe gap. A2017alloy was formed by extending continuously at the semisolid state on SRS machine. Throughcontrolling pouring temperature, semisolid forming and extending extrusion was combined organically.A2017 product with fine surface and rectangular transection of 14 mm x 25 mm was obtained. Bycontrast to the national standard, the fracture strength and elongation of A2017 products producedfrom extending semisolid extrusion have been improved with an increase of 100 MPa and 29%,respectively.
基金supported by the National Natural Science Foundation of China(61971165)the Key Research and Development Program of Hubei Province(2020BAB113)。
文摘Previous deep learning-based super-resolution(SR)methods rely on the assumption that the degradation process is predefined(e.g.,bicubic downsampling).Thus,their performance would suffer from deterioration if the real degradation is not consistent with the assumption.To deal with real-world scenarios,existing blind SR methods are committed to estimating both the degradation and the super-resolved image with an extra loss or iterative scheme.However,degradation estimation that requires more computation would result in limited SR performance due to the accumulated estimation errors.In this paper,we propose a contrastive regularization built upon contrastive learning to exploit both the information of blurry images and clear images as negative and positive samples,respectively.Contrastive regularization ensures that the restored image is pulled closer to the clear image and pushed far away from the blurry image in the representation space.Furthermore,instead of estimating the degradation,we extract global statistical prior information to capture the character of the distortion.Considering the coupling between the degradation and the low-resolution image,we embed the global prior into the distortion-specific SR network to make our method adaptive to the changes of distortions.We term our distortion-specific network with contrastive regularization as CRDNet.The extensive experiments on synthetic and realworld scenes demonstrate that our lightweight CRDNet surpasses state-of-the-art blind super-resolution approaches.
文摘The effects of Sr addition and pressure increase on the microstructure and casting defects of a low-pressure die cast （LPDC） AISi7Mg0.3 alloy have been studied. Metallographic and image analysis techniques have been used to quantitatively examine the microstructural changes and the amount of porosity occurring at different Sr levels and pressure parameters. The results indicate that an increase in the filling pressure induces lower heat dissipation of the liquid close to the die/core surfaces, with the formation of slightly greater dendrite arms and coarser eutectic Si particles. On the other hand, the increase in the Sr level leads to finer microstructural scale and eutectic Si. The analysed variables, within the experimental conditions, do not affect the morphology of eutectic Si particles. Higher applied pressure and Sr content generate castings with lower amount of porosiW. However, as the filling pressure increases the flow of metal inside the die cavity is more turbulent, leading to the formation of oxide films and cold shots. In the analysed range of experimental conditions, the design of experiment methodology and the analysis of variance have been used to develop statistical models that accurately predict the average size of secondary dendrite arm spacing and the amount of porosity in the low-pressure die cast AISiTMg0.3 alloy.
文摘Sparse representation has attracted extensive attention and performed well on image super-resolution（SR） in the last decade. However, many current image SR methods face the contradiction of detail recovery and artifact suppression. We propose a multi-resolution dictionary learning（MRDL） model to solve this contradiction, and give a fast single image SR method based on the MRDL model. To obtain the MRDL model, we first extract multi-scale patches by using our proposed adaptive patch partition method（APPM）. The APPM divides images into patches of different sizes according to their detail richness. Then, the multiresolution dictionary pairs, which contain structural primitives of various resolutions, can be trained from these multi-scale patches.Owing to the MRDL strategy, our SR algorithm not only recovers details well, with less jag and noise, but also significantly improves the computational efficiency. Experimental results validate that our algorithm performs better than other SR methods in evaluation metrics and visual perception.
基金the National Key Research and Development Program of China(No.2019YFB1405900)。
文摘Image super-resolution(SR)is an important technique for improving the resolution and quality of images.With the great progress of deep learning,image super-resolution achieves remarkable improvements recently.In this work,a brief survey on recent advances of deep learning based single image super-resolution methods is systematically described.The existing studies of SR techniques are roughly grouped into ten major categories.Besides,some other important issues are also introduced,such as publicly available benchmark datasets and performance evaluation metrics.Finally,this survey is concluded by highlighting four future trends.
基金the Natural Science Foundation of Jiangsu Province (No.BK2004151).
文摘Super-Resolution (SR) technique means to reconstruct High-Resolution (HR) images from a sequence of Low-Resolution (LR) observations,which has been a great focus for compressed video. Based on the theory of Projection Onto Convex Set (POCS),this paper constructs Quantization Constraint Set (QCS) using the quantization information extracted from the video bit stream. By combining the statistical properties of image and the Human Visual System (HVS),a novel Adaptive Quantization Constraint Set (AQCS) is proposed. Simulation results show that AQCS-based SR al-gorithm converges at a fast rate and obtains better performance in both objective and subjective quality,which is applicable for compressed video.
基金Supported by the National Natural Science Foundation of China(61405191)
文摘A maximum a posteriori（ MAP） algorithm is proposed to improve the accuracy of super resolution（ SR） reconstruction in traditional methods. The algorithm applies both joints image registration and SR reconstruction in the framework,but separates them in the process of iteratiion. Firstly,we estimate the shifting parameters through two lowresolution（ LR） images and use the parameters to reconstruct initial HR images. Then,we update the shifting parameters using HR images. The aforementioned steps are repeated until the ideal HR images are obtained. The metrics such as PSNR and SSIM are used to fully evaluate the quality of the reconstructed image. Experimental results indicate that the proposed method can enhance image resolution efficiently.
文摘In this work, we describe a new multiframe Super-Resolution（SR） framework based on time-scale adaptive Normalized Convolution（NC）, and apply it to astronomical images. The method mainly uses the conceptual basis of NC where each neighborhood of a signal is expressed in terms of the corresponding subspace expanded by the chosen polynomial basis function. Instead of the conventional NC, the introduced spatially adaptive filtering kernel is utilized as the applicability function of shape-adaptive NC, which fits the local image structure information including shape and orientation. This makes it possible to obtain image patches with the same modality,which are collected for polynomial expansion to maximize the signal-to-noise ratio and suppress aliasing artifacts across lines and edges. The robust signal certainty takes the confidence value at each point into account before a local polynomial expansion to minimize the influence of outliers.Finally, the temporal scale applicability is considered to omit accurate motion estimation since it is easy to result in annoying registration errors in real astronomical applications. Excellent SR reconstruction capability of the time-scale adaptive NC is demonstrated through fundamental experiments on both synthetic images and real astronomical images when compared with other SR reconstruction methods.
文摘The signal direction of arrival (DOA) estimate algorithm based on the eigendecomposition of the modified covariance matrix is introduced in this paper. A field test system consisting of a 4-element linear array and a meter band radar is also presented, which is applied to the experimental studies of the algorithms in the practical performances. The results of the test indicate that when SNR is only 5.85 dB, two airplanes being 0.25 beam width apart in azimuth can be resolved clearly.
基金supported by the National Natural Science Foundation of China(Nos.52008198,51425804,U20A20283,and U1813222)the Shenzhen International Cooperation Research Program(No.GJHZ20200731095009029)+2 种基金the Shenzhen Science and Technology Program(Nos.RCBS20210609103823048 and KJZD20230923114916032)the Foundation of the Department of Science and Technology of Guangdong Province(No.2019TQ05Z654)the Guangdong Provincial Key Laboratory of Construction Robotics and Intelligent Construction(No.2022KSYS013),China.
文摘Time-synchronous-averaging(TSA)is based on the idea of denoising by averaging,and it extracts the periodic components of a quasiperiodic signal and keeps the extracted waveform undistorted.This paper studies the mathematical properties of TSA,where three propositions are given to reveal the nature of TSA.This paper also proposes a TSA-spectrum based on super-resolution analysis and it decomposes a signal without using any base function.In contrast to discrete Fourier transform spectrum(DFT-spectrum),which is a spectrum in frequency domain,TSA-spectrum is a period-based spectrum,which can present more details of the cross effects between different periodic components of a quasiperiodic signal.Finally,a case study is carried out using bearing fault analysis to illustrate the performance of TSA-spectrum,where the rotation speed fluctuation of the shaft is estimated,which is about 0.12 ms difference.The extracted fault signals are presented and some insights are provided.We believe that this paper can provide new motivation for TSA-spectrum to be widely used in applications involving quasiperiodic signal processing(QSP).
基金the National Key R&D Program of China(No.2019YFB1405900)the National Natural Science Foundation of China(No.62172035,61976098)。
文摘Although most of the existing image super-resolution(SR)methods have achieved superior performance,contrastive learning for high-level tasks has not been fully utilized in the existing image SR methods based on deep learning.This work focuses on two well-known strategies developed for lightweight and robust SR,i.e.,contrastive learning and feedback mechanism,and proposes an integrated solution called a split-based feedback network(SPFBN).The proposed SPFBN is based on a feedback mechanism to learn abstract representations and uses contrastive learning to explore high information in the representation space.Specifically,this work first uses hidden states and constraints in recurrent neural network(RNN)to implement a feedback mechanism.Then,use contrastive learning to perform representation learning to obtain high-level information by pushing the final image to the intermediate images and pulling the final SR image to the high-resolution image.Besides,a split-based feedback block(SPFB)is proposed to reduce model redundancy,which tolerates features with similar patterns but requires fewer parameters.Extensive experimental results demonstrate the superiority of the proposed method in comparison with the state-of-the-art methods.Moreover,this work extends the experiment to prove the effectiveness of this method and shows better overall reconstruction quality.
基金Supported by the National Natural Science Foundation of China(No.61901183)Fundamental Research Funds for the Central Universities(No.ZQN921)+4 种基金Natural Science Foundation of Fujian Province Science and Technology Department(No.2021H6037)Key Project of Quanzhou Science and Technology Plan(No.2021C008R)Natural Science Foundation of Fujian Province(No.2019J01010561)Education and Scientific Research Project for Young and Middle-aged Teachers of Fujian Province 2019(No.JAT191080)Science and Technology Bureau of Quanzhou(No.2017G046)。
文摘Convolutional neural networks(CNNs) have shown great potential for image super-resolution(SR).However,most existing CNNs only reconstruct images in the spatial domain,resulting in insufficient high-frequency details of reconstructed images.To address this issue,a channel attention based wavelet cascaded network for image super-resolution(CWSR) is proposed.Specifically,a second-order channel attention(SOCA) mechanism is incorporated into the network,and the covariance matrix normalization is utilized to explore interdependencies between channel-wise features.Then,to boost the quality of residual features,the non-local module is adopted to further improve the global information integration ability of the network.Finally,taking the image loss in the spatial and wavelet domains into account,a dual-constrained loss function is proposed to optimize the network.Experimental results illustrate that CWSR outperforms several state-of-the-art methods in terms of both visual quality and quantitative metrics.
基金supported by the Natural Science Foundation of Shandong Province (Y2007F36)the National Natural Science Foundation of China (50872075)
文摘Ca0.4Sr0.6Bi3.95Nd0.05Ti4O15 （C0.4S0.6BNT） ferroelectric thin films were prepared on Pt/Ti/SiO2/Si substrates by sol-gel method. Effect of annealing process （time and temperature） on structures and ferroelectric properties of C0.4S0.6BNT thin film was investigated. The relative intensity of （200） peak increased first then decreased with annealing temperature and became predominant at 800 ℃. In contrast, no evident change could be observed in the （001） peak. The remnant polarization （Pr） and coercive field （Ec） for C0.4S0.6BNT film annealed at 800℃ for 5 min were 21.6μC/cm2 and 68.3 kV/cm, respectively.
